'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__	:   Optional[int]										=       {
    "configuration_xlm_roberta_xl": [
        "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaXLConfig",
        "XLMRobertaXLOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__	:   List[str]										=       [
          "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMRobertaXLForCausalLM",
          "XLMRobertaXLForMaskedLM",
          "XLMRobertaXLForMultipleChoice",
          "XLMRobertaXLForQuestionAnswering",
          "XLMRobertaXLForSequenceClassification",
          "XLMRobertaXLForTokenClassification",
          "XLMRobertaXLModel",
          "XLMRobertaXLPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta_xl import (
          XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaXLConfig,
          XLMRobertaXLOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta_xl import (
                XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaXLForCausalLM,
                XLMRobertaXLForMaskedLM,
                XLMRobertaXLForMultipleChoice,
                XLMRobertaXLForQuestionAnswering,
                XLMRobertaXLForSequenceClassification,
                XLMRobertaXLForTokenClassification,
                XLMRobertaXLModel,
                XLMRobertaXLPreTrainedModel,
            )

else:
      import sys

      lowercase__	:   Optional[int]										=       _LazyModule(__name__, globals()["__file__"], _import_structure)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_big_bird import BigBirdTokenizer
else:
						__UpperCamelCase     		=							None

__UpperCamelCase     		=							logging.get_logger(__name__)
__UpperCamelCase     		=							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase     		=							{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCamelCase     		=							{
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}


__UpperCamelCase     		=							'▁'







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		VOCAB_FILES_NAMES
			_UpperCamelCase		:       int 			=		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:       List[Any] 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:       Optional[Any] 			=		BigBirdTokenizer
			_UpperCamelCase		:       Dict 			=		['input_ids', 'attention_mask']
			_UpperCamelCase		:       List[int] 			=		[]




			def __init__(				self   ,    snake_case=None   ,    snake_case=None   ,    snake_case="<unk>"   ,    snake_case="<s>"   ,    snake_case="</s>"   ,    snake_case="<pad>"   ,    snake_case="[SEP]"   ,    snake_case="[MASK]"   ,    snake_case="[CLS]"   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else bos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else eos_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else unk_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else pad_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else cls_token
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else mask_token

						super().__init__(
						    snake_case   ,    tokenizer_file=snake_case   ,    bos_token=snake_case   ,    eos_token=snake_case   ,    unk_token=snake_case   ,    sep_token=snake_case   ,    pad_token=snake_case   ,    cls_token=snake_case   ,    mask_token=snake_case   ,    **snake_case   ,    )

						UpperCamelCase__       =							vocab_file
						UpperCamelCase__       =							False if not self.vocab_file else True




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]
						if token_ids_a is None:
									return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = False  ):

						'''simple docstring'''


						if already_has_special_tokens:
									if token_ids_a is not None:
												raise ValueError(
												    "You should not supply a second sequence if the provided sequence of "
												    "ids is already formatted with special tokens for the model."  )
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is None:
									return [1] + ([0] * len(snake_case  )) + [1]
						return [1] + ([0] * len(snake_case  )) + [1] + ([0] * len(snake_case  )) + [1]




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep  ) * [0]
						return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

						if not os.path.isdir(snake_case  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
						UpperCamelCase__       =							os.path.join(
						    snake_case   ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case  ):
									copyfile(self.vocab_file   ,    snake_case  )

						return (out_vocab_file,)


